Multi-resolution attention convolutional neural network for crowd counting

Zhang, Youmei; Zhou, Chunluan; Chang, Faliang; Kot, Alex C.

doi:10.1016/j.neucom.2018.10.058

Cited by 67 publications

(28 citation statements)

References 30 publications

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“…The adaptability of mentioned network is not fully utilized. Recently, some researchers [33]- [35] have tried to resolve this problem and they have begun to explore the generation of adaptive features and the effect is improved but not obvious. Besides, pixel-wise Euclidean loss is widely used in crowd counting network which regards a pixel as isolated.…”

Section: B Cnn-based Methodsmentioning

confidence: 99%

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Global Context Instructive Network for Extreme Crowd Counting

Wang

Wang²,

Bai

et al. 2020

IEEE Access

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Crowd counting has gained popularity due to wide applications, such as intelligent security, and urban planning. However, scale variation and perspective distortion make it a challenging task. Most existing works focus on multi-scale feature extraction to address the challenge of scale variation and perspective distortion. In this paper, we propose a novel Global Context Instructive Network (GCINet), which devotes to making full use of extracted features and obtaining precise counts. The main contributions are four folds. First, we construct a three-column Feature Processor to generate features with different scales. Second, an Instructive Module is proposed to introduce global context which is the substance for generating adaptive features. Based on global context, the three-column Feature Processor constitutes an adaptive feature generator. Third, a novel loss function which integrates Euclidean distance and spatial correlation is proposed to enhance the spatial correlation and consistency between pixels. We no longer regard a pixel as an independent point in the calculation, but consider the neighborhood space of the pixel to achieve complementary effects. Finally, we conduct experiments on ShanghaiTech dataset, UCF_CC_50 dataset, UCF_QNRF dataset and UCSD dataset which show that our approach achieves state-of-the-art performance. INDEX TERMS Crowd counting, density map, adaptive generator, global context instructive network (GCINet).

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Section: B Cnn-based Methodsmentioning

confidence: 99%

“…L(θ ) is the Euclidean distance between predicted density map and ground truth. L SCL proposed in [33] is the spatial correlation loss.…”

Section: E Loss Functionmentioning

confidence: 99%

Global Context Instructive Network for Extreme Crowd Counting

Wang

Wang²,

Bai

et al. 2020

IEEE Access

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“…Abstractive text summary using a generative adversarial network was done by the authors in [51], while the authors in [52] proposed a CNN-based technique to obtain high representational features for the detection of secondary protein structures. In order to further improve accuracy, researchers used CNN-based crowd-counting techniques [21,53,54]. Counting through CNN employs convolution, pooling, Rectified Linear Unit (RelU), and Fully Connected Layers (FCLs) to extract features that are used to obtain the density map [55].…”

Section: Counting By Cnnmentioning

confidence: 99%

“…Crowd-counting techniques face many challenges, such as high cluttering, varying illumination, varying object density, severe occlusion, and scale variation caused by different perspectives [18][19][20][21][22]. For instance, high cluttering can distort the resolution of an estimated map, and light illumination can reduce its accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Ilyas

Shahzad

Kim

2019

Sensors

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Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).

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